Facial recognition using 3d model

ABSTRACT

Technologies are described for reconstructing facial models which are preserved images or images captured from security cameras. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing facial models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of a face to be described. The one or more feature vector representations of a target face can be used individually or in combination with other descriptors for recognition, retrieval, and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

RELATED APPLICATIONS

This application claims priority of U.S. Patent Application No. 63/173,018 filed Apr. 9, 2021, and entitled, “DOCUMENT SEARCH USING 3D MODEL RECONSTRUCTION,” which is incorporated herein by reference.

FIELD

This application relates to the field of facial recognition in general. More specifically, this specification applies to the use of facial recognition using three-dimensional (3D) models reconstructed from target images.

BACKGROUND

Images taken from low quality security cameras make identification of individuals within a video sequence difficult. For example, the cameras only capture a portion of the face from a specific angle. In addition, due to the location of security cameras, which attempt to capture large areas of a location, the size of the face within the image is often small. Additionally, portions of the face are often obscured. Analysis of these images for the purpose of facial recognition poses a challenge to current systems.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Various technologies are described herein for reconstructing facial models, for example facial models of individuals using preserved images or images captured from security cameras. In some implementations, the reconstructed models can be compared to existing models and/or other reconstructed models based on physical geometry. This comparison can be used for identification of a target face through model retrieval or for classification of the face through model clustering. In some example implementations, these technologies can be applied to improve facial recognition. In some example implementations, these technologies can also be applied to transform facial models into high resolution facial images. These high-resolution facial images can have a higher resolution than original images used in constructing the facial models. For example, an approximation of an individual face can be reconstructed as a point cloud, for example, from a multi-view image set. Facial recognition can be improved (e.g., more accurate and effective) by using the reconstructed models. The use of 3D point cloud models can allow both local and global geometric properties of the face to be described in one or more feature vector representations. The one or more feature vector representations of the face can be used individually or in combination with other descriptors for identification and classification tasks.

As used herein, an object can be a face, for example a face of an individual.

Additionally, neural networks can be used to allow the encoding of point clouds into one or more feature vectors that are abstract multi-dimensional or latent space representations of the model. Once encoded as one or more feature vectors within the neural network, tasks such as classification and segmentation can be performed on the one or more feature vectors. A direct method for the generation of the one or more feature vectors representing these point clouds is described herein. These encoded representations of the models stand as one or more feature vectors that can be directly compared or combined with other attributes for further analysis.

In some implementations, the one or more feature vectors can be added to collections as metadata providing additional tools for analysis. For example, document collections, transformed into point clouds, may be augmented with these one or more feature vectors. In some implementations, only the one or more feature vectors are stored. In this case, the reconstructed point cloud serves as an intermediary representation between the multi-view image and the one or more feature vectors and may be reconstructed as required.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example environment for reconstructing physical objects which are represented or preserved in line drawings.

FIG. 2 is an example epipolar geometry having orthogonally aligned views.

FIG. 3 depicts an example of multi-view images placed orthogonally around a cube that represents a model space.

FIG. 4 depicts an example of a point chosen on a side view of an object and projected as a vector into a model space.

FIG. 5 depicts an example of multiple points chosen on a side view of an object and projected on a vector into a model space with potential candidates for a final point cloud.

FIG. 6 depicts one point added to the model space of a model point cloud.

FIG. 7 depicts an example of a point chosen on a front view of an object and projected as a vector into a model space, resulting in a point added to the model space of a model point cloud.

FIG. 8A depicts an example set of multi-view images.

FIG. 8B depicts an example point cloud.

FIG. 9 depicts an example environment for reconstructing physical objects for the one or more feature vector comparisons.

FIG. 10 depicts an example environment for reconstructing physical objects for document retrieval.

FIG. 11 depicts an example environment for reconstructing physical objects for document retrieval, using clustering.

FIG. 12 depicts an example environment for reconstructing images for facial recognition.

FIG. 13 is a diagram depicting an example process for reconstructing physical objects.

FIG. 14 is a diagram depicting an example process for document search using one or more feature vectors.

FIG. 15 is a diagram depicting an example process for reconstructing a facial model.

FIG. 16 is a diagram of an example computing system in which some described embodiments can be implemented.

DETAILED DESCRIPTION

The following description is directed to technologies for reconstructing physical objects, including human faces, which are captured, represented, or preserved in video feeds, pictorial records, including photographic images or line drawings. For example, an approximation of an object can be reconstructed as a point cloud; for example, the technologies can use the multi-view images or line drawings of an original object to create a point cloud that closely approximates the surface of the original physical object. This approximation of the original surface is similar to the original object and allows more accurate comparisons to be performed than using the single-view or multi-view images or line drawings themselves.

In typical object reconstruction solutions, the reconstructed images are either a mesh object, a smooth and well (or exaggerated) contoured image, a line drawing, or a silhouette. With the mesh objects, the faces that define the outer object surface are neither uniform nor consistent. A naive method of generating a point cloud from a mesh model is to simply take the vertices of the model and use these as points in a point cloud. In the case of a cube, this would create only six points. No points would be located on the faces of the cube or along the cube's edges.

The typical object reconstruction solutions also cannot create a point cloud when the original model is unavailable. They require images to be taken from a consistent view and do not address scaling or rotation invariant features.

The technologies described herein provide improvements over such typical object reconstruction solutions. For example, the technologies can create a point cloud that represents the original model, e.g., by using the multi-view images or line drawings of the original object to create a point cloud that closely approximates the surface of the original physical object. The point cloud is a collection of points in a space which are distributed across the surface of the original 3D model. Points located on the surface and edge of the object provide context to the overall shape of the object.

Additionally, the technologies described herein also provide improvements with the use of neural networks.

FIG.

depicts an example environment

for reconstructing models of physical objects which are represented or preserved in line drawings. The example environment

includes multi-view images 110 of an object, an airplane in this example. The multi-view images 110 can also be referred to as multi-view representation. The example multi-view images 110 include a front view, bottom view and right-side view of the object. Although three views are shown in this example, the multi-view images 110 can have any other number of views, for example, it can also include a top view and a left-side view. Although line drawing images 110 are depicted in this example, other pictorial images can also be used. The object in the multi-view images 110 is reconstructed into a 3D model as point cloud 120. The point cloud 120 is inputted into a neural network 130, e.g., without loss of generality, an image neural network or a convolutional neural network having multiple convolutional layers. The neural network 130 can classify (at 140) the point cloud 120 and transform the point cloud 120 into one or more feature vectors 150. The one or more feature vectors 150 represent the transformation of the point cloud 120 into different latent space representations. It is likewise within the scope of this disclosure to transform the point cloud into a set of one or more feature vectors 150, logically treating the set as one vector. The one or more feature vectors 150 can be a statistical representation of the point cloud 120. As will be described in more detail herein, the one or more feature vectors 150 can be used to assess the similarity of objects or images, and/or for image and/or document retrieval.

Although the example environment

shows the object represented in line drawings, the object can be represented in another pictorial record in place of the line drawings. When the representative line drawings lack certain views, these views can be generated or obtained, for example, from similar images or generic images. In some implementations, missing like drawings can be skipped or ignored.

In some implementations, the 3D model of the object can be a point cloud. Creating such a point cloud, in at least some scenarios, can result in a model of the physical object that is sufficiently similar to the physical object (or an original model of the physical object) to perform comparison and retrieval of a document containing the multi-view images, for example, by matching the one or more feature vectors. In at least some scenarios, document retrieval based on model reconstruction using a point cloud and its one or more feature vectors can be more effective than retrieval using the multi-view images on which the point cloud is based.

In some implementations, a point cloud can be created using epipolar geometry. For example, as depicted in example 200 in FIG. 2, views can be orthogonally aligned. This implies a single point within a view can be represented as a line in any orthogonal image or view. A point w on the surface of the model in space R₃ can be selected from a point x in a drawing image in view R₂. This point can then be projected as a line v from x to w. The vector v can be viewed as a line L in an orthogonal view R₂, where point x′ is the projection of w onto L.

In FIGS. 3 to 7, using the multi-view images of an airplane as an example, the images from the side view (on viewport R₁), front view (on viewport R₂), and bottom view (on viewport R₃) are placed orthogonally around a cube that represents the R₄ model space. First, a point 310 is chosen on the side view of the object (on viewport R₁) and projected as a vector 410 into the model space R₄, as depicted in FIG. 4. This vector may be seen (projected) on the front view (on viewport R₂) and bottom view (on viewport R₃) as lines L. Since the technique generates the shell of the model, only points that are on the periphery of the images (e.g., surface and edge) are considered. In FIG. 5, by way of example, there are at least four points (502-510 Xs on vector 410) that are on the edges of both the front view (512-514 Xs on viewport R₂) and bottom view (522-524 Xs on viewport R₃). Each of these points 502-510 Xs on vector 410, e.g., point 510, can be candidates for the final point cloud. FIG. 6 depicts one point 510 added to the R₄ space of the model point cloud. FIG. 7 depicts another example where another point 720 is chosen from the front view (on viewport R₂) and projected again as a vector 710 into the model space R₄ of the model point cloud.

Using this technique, points can be iteratively sampled from the multi-view images to construct the final point cloud. The number of points that can be sampled can vary, for example, 256 points to 256,000 points. The upper limit on the number of points generated can depend on the resolution of the multi-view images. When the source images are in grayscale or color, the sampled points can sample the image grayscale or color as well. In some experimentations, a cloud point with 2,048 points can provide strongest overall retrieval, as measured by normalized discounted cumulative gain, however the number of points identified in these experiments is not intended to be a limiting factor in implementation.

An example multi-view set of images 800, as shown in FIG. 8A, can include a bottom view image 810, right view image 820, front view image 830, top view image 840, left view image 850, and back view image 860. The images included in the multi-view set of images 800 are not limited to these images and can include additional images. There are also cases where an original multi-view document contains fewer images. In some implementations, when a view is missing, a substitute can be generated. For example, an opposite but substantially similar view can be used in place of a missing view, e.g., substituting the right view image 820 for the left image 850. In some implementations, substitutions can include image manipulations such as horizontal or vertical flips or some other rotations. For cases where both a view and the opposite view are missing, a generic shape can be substituted. For example, a circular mask can be used for the top/under views of bowls and bottles and a square mask can be used for statues. For some objects, a square mask can be used missing front/back and left/right views. If the object's top or under views show sufficient radial symmetry, it is possible to use a front view in place of the side or back views. In some implementations, when the images are not to scale, they must be aligned such that the bounding boxes for each mask form a cuboid box. A common vertical, horizontal, and depth are established, and the images are scaled to meet these dimensions. If a mask is substituted for one or more views, the masks can be scaled as well. For some cases where a view is missing, an image from a similar multi-view document can also be used for substitution.

With respect to multi-view images, as shown in FIG. 3 and FIG. 8, it may not be required that a point 310 be chosen at random from the image. The selection of point 310 can be based on techniques that use both internal (e.g., texture region 822) and external knowledge for sampling and point distribution. For example, the techniques can sample more points for specified local regions such as texture regions 822, or the techniques can sample more points for surface with contours 826 than flat surfaces 824, etc. In the examples where the multi-view images contain environmental details (e.g., areas surrounding the main object), the techniques can distinguish and focus more on the object or specified local regions, for example, and sample more points on the object or the specified local regions.

As shown in FIG. 8B, point cloud 870 representation can include arbitrary point cloud size, an arbitrary number of points, a non-uniform distribution of points, and an arbitrary scale or rotation. In a neural network setting, point clouds can be represented as a vector of points, in which the representation of individual points can be arbitrarily sorted. The vector representation of the individual points within the point cloud can vary depending on whether the point cloud is in color, grayscale, or black and white. The vector representation of the individual points within the point cloud can also include information in addition to position and color, such as normal vector, voxel or importance weight, and other descriptive elements. A neural network can generate a partitioning of the point set, abstract sets of points, or local features through a local feature learner. This can be accomplished through sampling and aggregation. By specifying the number of centroid points and the size of the neighborhood, point clouds can be up-sampled or down-sampled. The final result can be one or more feature vectors that summarize the point cloud. The techniques of the present disclosure can integrate both local and global features and include biased sampling of points to create the one or more feature vectors, such as object shape as well as local features such as corners, contours, density and resolution. The techniques can use both internal (e.g., texture 872) and external knowledge for sampling and point distribution. For example, the techniques can sample more points for specified local regions, or the techniques can sample more points for surface with dense regions of points 872 than sparse regions of points 874, etc. In the examples where the point cloud 870 contains dense regions of points 872 or sparse regions of points 874, the techniques can distinguish and focus more on the object or specified local regions, for example, and sample dense regions of points 872 on the object or the specified local regions. The techniques can encode the global and local features into one or more feature vectors.

In some implementations, as shown in FIG. 9, a final set of fully connected layers of a neural network can transform the one or more feature vectors 950 representations of the model into a final classification vector 955 that can represent the confidence that the original model belongs to this classification or distinctive feature. This portion of the network can be used to directly identify the classification of an object or can be trained to identify distinctive features of an object, such as whether it contains specific substructures (e.g., cones, wings, sharp edges, facial features, stylistic features, etc.).

In some implementations, the techniques of the present disclosure can train the network to create the one or more feature vectors 950 representations of the 3D model. The layer located immediately prior to the final layer can be a hidden layer, for example, of size 256. This layer can take the one or more feature vectors 950 representations of the 3D model as input and provide a classification or other distinctive feature recommendation as output. As the network is trained, this layer can be heavily biased to place the 3D model into a classification or identify the presence of a distinctive feature. This, in effect, can create one or more feature vectors 950 representations of the 3D model that are similar for similar classifications or distinctive features and create the encoding into the latent space. The encoding of the one or more feature vectors 950 representations of the 3D model can encapsulate common details from the classifications or distinctive features so that the one or more feature vectors 950 representations of the 3D model may be compared using, for example, cosine similarity or any other similarity measure, to determine similarity between 3D models and be used as a basis for similar model retrieval.

In FIG. 9, the example environment 900 can depict the pipeline for transforming the multi-view 2D images 910 into a reconstructed 3D model 920 and finally into one or more feature vectors 950 representations of the 3D model using a neural network model training as shown. For example, each set of multi-view 2D images 910 can be encoded as a reconstructed 3D model 920, represented as into one or more feature vectors 950 representations of the 3D model, and finally matched to the classifications or distinctive features associated with the multi-view 2D images 910. The classifications or distinctive features associated with the multi-view 2D images 910 may be based on a high-level grouping of the multi-view 2D images 910 (e.g., plane, car, bottle, face, person, etc.) For example, the classifications or distinctive features may be represented as a vector. Each of the classifications or distinctive features are represented as a single element in this vector. The multi-view 2D images 910 may belong to one or more classifications or distinctive features. Using standard neural network training methodologies, the full training set of multi-view 2D images 910 is used to refine the neural network to predict the correct classifications or distinctive features for any particular multi-view 2D images 910. Once the network is trained, it can be applied, for example, to multi-view 2D images 910 in the testing set. The techniques of the present disclosure can learn which points are more important, for example, for each class (or category) of object. The one or more feature vectors 950 representations of the 3D model corresponding to each collection of multi-view 2D images 910 can be extracted. The one or more feature vectors 950 representations of the 3D model can then be used in comparison using cosine similarity or any other similarity measure, for example, for retrieval and clustering tasks. Once the network has been trained, the testing set can be evaluated and the one or more feature vectors 950 representations of the 3D model corresponding to each collection of multi-view 2D images 910 can be stored along with any additional descriptive data, such as classifications or distinctive features. Through extensive experimentations and evaluations, the techniques of the present disclosure using the one or more feature vectors 950 representations of the 3D model are shown as more accurate and effective than other techniques, for example, for image and document retrievals.

The example environment 900 can also include reconstructing physical objects for feature vector comparison. For example, the example environment 900 includes multi-view images 910 of an object, an airplane in this example, which are represented or preserved in line drawings. The multi-view images 910 can also be referred to as multi-view representation. The example multi-view images 910 include a front view, bottom view and right-side view of the object. Although three views are shown in this example, the multi-view images 910 can have any other number of views, for example, it can also include a top view and a left-side view. Although line drawing images 910 are depicted in this example, other pictorial images can also be used. The object in the multi-view images 910 is reconstructed into a 3D model as point cloud 920. The point cloud 920 is inputted into a neural network 930, e.g., an image neural network. In some implementations, an attention mechanism 935 can be included. For example, the one or more feature vector outputs from the multi-view images 910 processed by network 930 can be fed to a fully connected general attention layer 935. This layer can learn a weighting for each feature to allow the one or more feature vectors that contribute more strongly to a final answer to be weighted more highly. The learned weighting of the one or more feature vectors can allow more discriminative views to contribute more to the final image analysis results. The neural network 930 can classify the point cloud 920 (at 940), and transform the point cloud 920 into one or more feature vectors 950. The one or more feature vectors 950 can be a statistical representation of the point cloud 920. The one or more feature vectors 950 can be used to assess the similarity of objects or images, and/or for image and/or document retrieval. In this example, the one or more feature vectors 950 can be compared (at 960) with the one or more feature vectors 150 (see FIG. 1). A result 970 can indicate whether and how the one or more feature vectors 150 and 950 are similar. The result can thus indicate whether the objects represented by the multi-view 2D images 110 and 910 are similar.

FIG. 10 depicts an example environment 1000 for reconstructing physical objects for document retrieval. The example environment 1000 includes multi-view images 1010 of an object, an airplane in this example, which are represented or preserved in line drawings. The multi-view images 1010 can also be referred to as multi-view representation. The example multi-view images 1010 include a front view, bottom view and right-side view of the object. Although three views are shown in this example, the multi-view images 1010 can have any other number of views, for example, it can also include a top view and a left-side view. Although line drawing images 1010 are depicted in this example, other pictorial images can also be used. The object in the multi-view images 1010 is reconstructed into a point cloud 1020. The point cloud 1020 is inputted into a neural network 1030, e.g., an image neural network. In some implementations, an attention mechanism 1035 can be included. For example, the one or more feature vector outputs from the multi-view images 1010 processed by network 1030 can be fed to a fully connected general attention layer 1035. This layer can learn a weighting for each feature to allow the one or more feature vectors that contribute more strongly to a final answer to be weighted more highly. The learned weighting of the one or more feature vectors can allow more discriminative views to contribute more to the final image analysis results. The neural network 1030 can classify the point cloud 1020 (at 1040) and transform the point cloud 1020 into one or more feature vectors 1050. The one or more feature vectors 1050 can be a statistical representation of the point cloud 1020. The one or more feature vectors 1050 can be used to assess the similarity of objects or images, and/or for image and/or document retrieval. In this example, the one or more feature vectors 1050 can be used in a document search. For example, the one or more feature vectors 1050 can be compared (at 1060, e.g., using one or more feature vectors 1050 as a query vector in a search engine) with one or more feature vectors 1050 in a feature vector database (or library) 1070. In some implementations, the result 1080 from the comparison can include one or more vector features most matched with the one or more feature vectors 1050. The result 1080 can be used to search a document database 1090, for example, for the document(s) associated with the one or more feature vectors in the result 1080. In some implementations, the feature vector database 1070 can be coupled to document database 1090. In some implementations, the one or more feature vectors 1050 can be added to the document database 1090 as metadata. The document database 1090 can include any type of document, for example, texts, images, videos, etc.

In some implementations, the comparison 1060 can also use a classification or distinctive feature comparison, or a combination of these comparisons. The one or more feature vectors 1050 can be compared using, for example, cosine similarity, Euclidean distance, or any other similarity measure without limitation. The one or more feature vectors 1050 can be used as a query vector, with all other one or more feature vectors 1050 in the feature vector database 1070 sorted by cosine similarity, Euclidean distance, or any other similarity measure.

FIG. 11 depicts an example environment 1100 for reconstructing physical objects for document retrieval, using clustering. Clustering can be desired in some implementations, for example, in clustering of original objects or their multi-view 2D images. The example environment 1100 includes clusters of multi-view images 1110. One cluster can include airplanes in this example. Other clusters can include other different objects or objects clustered based on distinctive features. The objects in the multi-view images 1110 of all objects in each cluster are reconstructed into respective 3D model point clouds 1120. The 3D model point clouds 1120 are inputted into one or more neural networks 1130, e.g., image neural networks. An attention mechanism 1135 can be included. For example, the one or more feature vector outputs from the multi-view images 1110 processed by networks 1130 can be fed to a fully connected general attention layer 1135. This layer can learn a weighting for each feature to allow the one or more feature vectors that contribute more strongly to a final answer to be weighted more highly. The learned weighting of the one or more feature vectors can allow more discriminative views to contribute more to the final image analysis results. The neural networks 1130 can then classify the 3D model point clouds 1120 (at 1140) and transform the 3D model point clouds 1120 into the one or more feature vectors 1150.

In some implementations, a dendrogram can be used to explore the hierarchical relationships of the one or more feature vectors. The one or more feature vectors can then be compared by progressively combining nearest neighbors into clusters. Initially each point can be considered its own cluster. To combine clusters, the ‘single point’ method can look for the closest points i and j from clusters u and v such that d(u, v)=min(∀i∈u∀j∈vd(u[i], v[j]), namely, a point in the first cluster closest to a point in the other cluster. By iteratively combining clusters, it is possible to identify model similarities that are not captured by direct retrieval. It should be noted that the encoded point cloud is the basis for the one or more feature vectors. The clustering is conducted over the one or more feature vectors generated in the last layer of the neural network model.

In some implementations, a dendrogram may be created can be based on the one or more feature vectors 850. Models that connect to other groupings can be related by grouping but not as closely to other models. The longer legs of the dendrogram leaves can indicate an overall weaker similarity between the models. The dendrogram can be divided into groupings, for example, groupings based on classifications (e.g., statues, bowls, etc.) or distinctive features. One or more groups of models can show no definitive clusters of models. It should still be noted that even with the inaccurate higher-level groupings, the nearest neighbors for most models can be still within the same classifications or distinctive features. Model clustering can show a much stronger similarity measure between individual models and groupings. Models that connect directly in the dendrogram have the strongest similarity. The shorter legs of the dendrogram leaves can indicate a stronger similarity between objects.

As described above, the techniques described herein can be implemented in image and document comparisons and searches. Document as used herein can include any type of document, for example, texts, images, videos, etc. Images can also include human bodies, faces, etc. As such, the techniques described herein can be used for facial recognition. For example, as shown in FIG. 12, the object 1214 and its multi-view images 1230-1270 can be those of human heads and faces. 3D model 1290 of these heads and faces can be reconstructed into point clouds and encoded into one or more feature vectors as described herein. Tasks such as classification and segmentation can also be performed. These one or more feature vectors can be used to perform facial recognition and searches.

Images taken from low quality security cameras make identification of individuals within a video sequence difficult. As shown in the example 1200 of FIG. 12, cameras can only capture a portion of the face 1214 from a specific angle. Due to the location of security cameras, which attempt to capture large areas of a location, the size of the face within the image is often small. Additionally, portions of the face are often obscured.

In some implementations, by tracking an individual, e.g., individual 1212, within a video feed, the facial features from the target individual 1212 within each image 1210 can be collected. The angle of the target individual 1212′s face relative to the camera can be determined for each image 1210, the image of the face 1214 can be extracted from the complete surveillance image 1210, and the facial images 1214 can be sorted (at 1220) from left to right and top to bottom to create a set of facial images 1230-1270 for the target individual 1212. Corresponding points (point 1241 shown as single example) within these images can be identified using similar geometrical mapping as previously disclosed and represented (at 1280) as 3D points within the facial model 1290. Because the correspondence between multiple facial images is used, these 3D points can create a point cloud model (not shown) that is of high resolution.

In some implementations, this point cloud can be used directly for facial recognition or can be transformed back into a facial image 1295 that is of higher resolution than the original images. The reconstructed facial images provide a super-resolution 3D model of the face, resulting in a dense point cloud representation that can be easily converted to an image of the face from any angle. The techniques of the present disclosure can combine multiple facial images, all of which are partial representations in low quality, into a single dense representation that fills in facial features and provides fine detail of the underlying face.

In some examples, not all contours require the same resolution for identification. Thus, some varied facial components can be sampled at a higher resolution than some others; and less meaningful and less varying facial components can be sampled at a lower rate. Sampling variation can provide for higher resolution when needed while reducing the overall computation. The choice to sample at a higher resolution may be guided by the information density, color information, and other data extracted directly from the sampled images. Additionally, external knowledge regarding the regions to be sample can be employed to guide the sampling process. External knowledge, without loss of generality, can be made available by the environment involved (e.g., nature scene or crowded mall or shadow or evenly lit), image capture capability (as dependent on the characteristics of the imaging device), application nature (surveillance of a given person), etc. For example, without loss of generality, assume that in a surveillance application we know that the person of interest 1212 should have a particular scar or a tattoo on their left cheek. In such a case, the region containing the left cheek would be sampled at a higher resolution so as to provide a better image of the expected potential marking.

Additionally, when tracking an individual within a video feed, the identified facial images of the target individual can be streamed to a facial reconstruction engine. Once a sufficient number of facial images have been collected, the facial reconstruction engine can begin sampling points. Any new facial images collected from the target individual can be added to the set of facial images for the target individual. As points are sampled from the progressively increasing set of facial images, the sampling rate of different facial regions can be varied so that the points generated from the initial facial images do not outweigh the points generated by the later facial images. This sampling can happen within the constraints of the super-resolution sampling previously discussed. In some implementations, the facial reconstruction engine can be implemented in block 960 of FIG. 9.

In the technologies described herein, methods can be provided for reconstructing physical objects which are represented or preserved in pictorial records, including photographic images or line drawings.

FIG. 13 is a diagram depicting an example process 1300 for reconstructing physical objects. At 1310, multi-view 2D images of an object to be reconstructed can be obtained. In some implementations, line drawings images can be obtained, for example, from the United States patent database, or generated. However, other pictorial records, e.g., photos, can also be used. When views are missing, substitutes can be generated as described above.

At 1320, the object in the multi-view images can be reconstructed into a 3D model point cloud. In some implementations, the point cloud can be inputted into a neural network, e.g., an image neural network. The neural network can classify the 3D model point cloud.

At 1330, the point cloud can be encoded into one or more feature vectors. The one or more feature vectors can be a statistical representation of the 3D model point cloud. In some embodiments, the process 1300 can integrate both local and global features and include bias sampling, such as object shape as well as local features such as corners, contours, density and resolution. The process 1300 can use both internal and external knowledge for sampling and point distribution. For example, the process can sample more points for surface with contours than flat surface, and focus more on the object, for example, sample more points on the object and not the surrounding environment.

FIG. 14 is a diagram depicting an example process 1400 for document search using one or more feature vectors. At 1410, one or more feature vectors are received, for example, one or more feature vectors as encoded in process 1300 of FIG. 13.

At 1420, the process 1400 can include searching for similar vectors stored in a vector database (or library). For example, the one or more feature vectors can be used as a query vector in a search engine. In some implementations, the one or more feature vectors can be compared using cosine similarity or any other distance measurement known in the art. In these implementations, the one or more feature vectors in the feature vector database can be sorted by cosine or any other distance measurement known in the art. In some implementations, each result can be considered a match if it shares the class of the query vector and not a match if it belongs to a different class.

At 1430, the process 1400 can include searching a document database (or library) for the documents associated with the one or more feature vectors considered as similar or matched at 1420. The documents can include any type of document, for example, texts, images, videos, etc. Images can also include human bodies, faces, etc.

FIG. 15 is a diagram depicting an example process 1500 for reconstructing a facial model. For example, at 1510, one or more video streams can be obtained. In some implementations, the video streams can be obtained from one or more security cameras. At 1520, a target individual can be isolated from the video streams. In some implementations, at 1530, a set of facial images for the target individual can be extracted. For example, as new video streams are received, new facial images from the new video streams can be extracted and added to the set of facial images.

At 1540, the set of facial images for the target individual can be ordered based on a facial pose of each facial image of the set of facial images. For example, the facial images can be sorted from left to right and top to bottom, as shown in FIG. 12. In some implementations, the facial images can be ordered using epipolar geometry.

At 1550, one or more source images from the set of facial images for the target individual can be selected. At 1560, for each selected source image, one or more points from the selected source image can be projected onto, for example two other facial images from the set of facial images. At 1570, one or more corresponding points between the two projected points can be identified, for example using the facial pose of the each of selected source image.

At 1580, the one or more corresponding points can be projected into a three-dimensional (3D) space, forming a point cloud. In some implementations, the point cloud can be encoded into one or more feature vector representations. The point cloud can be matched to one or more other feature vectors in a feature vector database.

Computing Systems

FIG. 16 depicts a generalized example of a suitable computing system 1600 in which the described innovations may be implemented. The computing system 1600 is not intended to suggest any limitation as to scope of use or functionality, as the innovations may be implemented in diverse general-purpose or special-purpose computing systems.

With reference to FIG. 16, the computing system 1600 includes one or more processing units 1610, 1615 and memory 1620, 1625. In FIG. 16, this basic configuration 1630 is included within a dashed line. The processing units 1610, 1615 execute computer-executable instructions. A processing unit can be a general-purpose central processing unit (CPU), processor in an application-specific integrated circuit (ASIC) or any other type of processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. For example, FIG. 16 shows a central processing unit 1610 as well as a graphics processing unit or co-processing unit 1615. The tangible memory 1620, 1625 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two, accessible by the processing unit(s). The memory 1620, 1625 stores software 1680 implementing one or more innovations described herein, in the form of computer-executable instructions suitable for execution by the processing unit(s).

A computing system may have additional features. For example, the computing system 1600 includes storage 1640, one or more input devices 1650, one or more output devices 1660, and one or more communication connections 1670. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing system 1600. Typically, operating system software (not shown) provides an operating environment for other software executing in the computing system 1600, and coordinates activities of the components of the computing system 1600.

The tangible storage 1640 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium which can be used to store information in a non-transitory way and which can be accessed within the computing system 1600. The storage 1640 stores instructions for the software 1680 implementing one or more innovations described herein.

The input device(s) 1650 may be a touch input device such as a keyboard, mouse, pen, or trackball, a voice input device, a scanning device, or another device that provides input to the computing system 1600. For video encoding, the input device(s) 1650 may be a camera, video card, TV tuner card, or similar device that accepts video input in analog or digital form, or a CD-ROM or CD-RW that reads video samples into the computing system 1600. The output device(s) 1660 may be a display, printer, speaker, CD-writer, or another device that provides output from the computing system 1600.

The communication connection(s) 1670 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing system on a target real or virtual processor. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Computer-executable instructions for program modules may be executed within a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unless the context clearly indicates otherwise, neither term implies any limitation on a type of computing system or computing device. In general, a computing system or computing device can be local or distributed, and can include any combination of special-purpose hardware and/or general-purpose hardware with software implementing the functionality described herein.

For the sake of presentation, the detailed description uses terms like “determine” and “use” to describe computer operations in a computing system. These terms are high-level abstractions for operations performed by a computer, and should not be confused with acts performed by a human being. The actual computer operations corresponding to these terms vary depending on implementation.

Example Implementations

Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially may in some cases be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods can be used in conjunction with other methods.

Any of the disclosed methods can be implemented as computer-executable instructions or a computer program product stored on one or more computer-readable storage media and executed on a computing device (i.e., any available computing device, including smart phones or other mobile devices that include computing hardware). Computer-readable storage media are tangible media that can be accessed within a computing environment (one or more optical media discs such as DVD or CD, volatile memory (such as DRAM or SRAM), or nonvolatile memory (such as flash memory or hard drives)). By way of example and with reference to FIG. 15, computer-readable storage media include memory 1520 and 1525, and storage 1540. The term computer-readable storage media does not include signals and carrier waves. In addition, the term computer-readable storage media does not include communication connections, such as 1570.

Any of the computer-executable instructions for implementing the disclosed techniques as well as any data created and used during implementation of the disclosed embodiments can be stored on one or more computer-readable storage media. The computer-executable instructions can be part of, for example, a dedicated software application or a software application that is accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software can be executed, for example, on a single local computer (e.g., any suitable commercially available computer) or in a network environment (e.g., via the Internet, a wide-area network, a local-area network, a client-server network (such as a cloud computing network), or other such network) using one or more network computers.

For clarity, only certain selected aspects of the software-based implementations are described. Other details that are well known in the art are omitted. For example, it should be understood that the disclosed technology is not limited to any specific computer language or program. For instance, the disclosed technology can be implemented by software written in C++, Java, Perl or any other suitable programming language. Likewise, the disclosed technology is not limited to any particular computer or type of hardware. Certain details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure.

Furthermore, any of the software-based embodiments (comprising, for example, computer-executable instructions for causing a computer to perform any of the disclosed methods) can be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means.

The disclosed methods, apparatus, and systems should not be construed as limiting in any way. Instead, the present disclosure is directed toward all novel and nonobvious features and aspects of the various disclosed embodiments, alone and in various combinations and sub combinations with one another. The disclosed methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the disclosed embodiments require that any one or more specific advantages be present or problems be solved.

The technologies from any example can be combined with the technologies described in any one or more of the other examples. In view of the many possible embodiments to which the principles of the disclosed technology may be applied, it should be recognized that the illustrated embodiments are examples of the disclosed technology and should not be taken as a limitation on the scope of the disclosed technology. Rather, the scope of the disclosed technology includes what is covered by the scope and spirit of the following claims. 

What is claimed is:
 1. A method for facial image reconstruction, performed by one or more computing devices, the method comprising: obtaining one or more video streams; extracting a set of facial images for a target individual from the video streams; ordering the set of facial images for the target individual based on a facial pose of each facial image of the set of facial images; selecting one or more source images from the set of facial images for the target individual; for each selected source image, projecting one or more points from the selected source image onto two other facial images from the set of facial images; identifying one or more corresponding points between the two projected points using the facial pose of the each of selected source image; and projecting the one or more corresponding points into a three-dimensional (3D) space.
 2. The method of claim 1, wherein the projecting the one or more corresponding points into a 3D space forms a point cloud.
 3. The method of claim 1, further comprising isolating a target individual from the video streams before the extracting a set of facial images.
 4. The method of claim 1, wherein the number of points projected into the 3D space is greater than a number of pixels of each facial image of the set of facial images for the target individual.
 5. The method of claim 1, wherein the extracting a set of facial images further comprises sampling one or more contours of each facial image of the extracted set of facial images.
 6. The method of claim 5, wherein a sampling rate for each facial image is varied based on image characteristics of a region of each facial image.
 7. The method of claim 5, wherein a sampling rate for each facial image is varied based on external information of a region of each facial image.
 8. A method for facial recognition, performed by one or more computing devices, the method comprising: obtaining one or more video streams; isolating a target individual from the video streams; progressively extracting a set of facial images for the target individual; ordering the set of facial images for the target individual based on a facial pose of each facial image of the set of facial images; selecting one or more source images from the set of facial images for the target individual; for each selected source image, projecting one or more points from the selected source image onto two other facial images from the set of facial images; identifying one or more corresponding points between the two projected points using the facial pose of the each of selected source image; projecting the one or more corresponding points into a three-dimensional (3D) space; and wherein the 3D space forms a point cloud.
 9. The method of claim 8, further comprises encoding the point cloud into one or more feature vector representations.
 10. The method of claim 9, further comprises matching the one or more feature vector representations to one or more other feature vectors in a feature vector database.
 11. The method of claim 8, wherein the progressively extracting a set of facial images comprises extracting new facial images from new video streams, and adding the new facial images to the set of facial images.
 12. The method of claim 8, wherein the number of points projected into the 3D space is greater than a number of pixels of each facial image of the set of facial images for the target individual.
 13. The method of claim 8, wherein the extracting a set of facial images further comprises sampling one or more contours of each facial image of the extracted set of facial images.
 14. The method of claim 13, wherein a sampling rate for each facial image is varied based on image characteristics of a region of each facial image.
 15. The method of claim 13, wherein a sampling rate for each facial image is varied based on external information of a region of each facial image.
 16. The method of claim 9, further comprising classifying the one or more feature vector representations with at least one object class using a neural network.
 17. One or more computing devices comprising: processors; and memory; the one or more computing devices configured, via computer-executable instructions, to perform retrieval of a document, the operations comprising: obtaining one or more video streams; isolating a target individual from the video streams; progressively extracting a set of facial images for the target individual; ordering the set of facial images for the target individual based on a facial pose of each facial image of the set of facial images; selecting one or more source images from the set of facial images for the target individual; for each selected source image, projecting one or more points from the selected source image onto two other facial images from the set of facial images; identifying one or more corresponding points between the two projected points using the facial pose of the each of selected source image; projecting the one or more corresponding points into a three-dimensional (3D) space; and wherein the 3D space forms a point cloud.
 18. The one or more computing devices of claim 17, further comprises encoding the point cloud into one or more feature vector representations.
 19. The one or more computing devices of claim 17, wherein the operations further comprising classifying the one or more feature vector representations with at least one object class using a neural network.
 20. The one or more computing devices of claim 17, further comprises matching the one or more feature vector representations to one or more other feature vectors in a feature vector database. 